Discovering implicit intention-level knowledge from natural-language texts |
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| Authors: | John Atkinson Anita Ferreira Elvis Aravena |
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| Affiliation: | 1. Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA;;2. Helix OpCo LLC, San Francisco, CA, USA;;3. Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA;;4. University of Washington Medical Center, Seattle, WA, USA;;5. Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA;;6. Children’s Hospital of Philadelphia, Philadelphia, PA, USA;;7. Department of Medicine, Division of Nephrology, Columbia University, New York, NY, USA;;8. Irving Institute for Clinical and Translational Research, Columbia University, New York, NY, USA;;9. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA;;10. Baylor Genetics Laboratories, Houston, TX, USA;;11. Veritas Genetics, Danvers, MA, USA;;12. UTHealth School of Public Health, Houston, TX, USA. |
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| Abstract: | In this paper, we propose a new approach to automatic discovery of implicit rhetorical information from texts based on evolutionary computation methods. In order to guide the search for rhetorical connections from natural-language texts, the model uses previously obtained training information which involves semantic and structural criteria. The main features of the model and new designed operators and evaluation functions are discussed, and the different experiments assessing the robustness and accuracy of the approach are described. Experimental results show the promise of evolutionary methods for rhetorical role discovery. |
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